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Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on…
Open-source data offers a scalable and transparent foundation for estimating vehicle activity and emissions in urban regions. In this study, we propose a data-driven framework that integrates MOVES and open-source GPS trajectory data,…
Understanding the evolution of radial sizes and instantaneous expansion speeds of coronal mass ejections (CMEs) is crucial for assessing their impact duration on Earth's environment. We introduce a non-conventional approach to derive the…
Noise contrastive estimation (NCE) is a popular method for training energy-based models (EBM) with intractable normalisation terms. The key idea of NCE is to learn by comparing unnormalised log-likelihoods of the reference and noisy…
The power sector is responsible for 32 percent of global greenhouse gas emissions. Data centers and cryptocurrencies use significant amounts of electricity and contribute to these emissions. Demand-side flexibility of data centers is one…
Electric power generation, transmission, and distribution systems are attracting a large amount of interest from researchers with the development of the smart grid technologies. A smart grid aims at effective control and conditioning of the…
This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local…
We present novel lower bounds on the mean square error (MSE) of the location estimation of an emitting source via a network where the sensors are deployed randomly. The sensor locations are modeled as a homogenous Poisson point process. In…
In this thesis, we study the Chiral Magnetic Effect (CME) and the Chiral Separation Effect (CSE) using lattice QCD simulations. We completely characterize the CSE in QCD using $2+1$ simulations of staggered quarks tuned at the physical…
Transactive or market-based coordination strategies have recently been proposed for controlling the aggregate demand of a large number of electric loads. Such schemes offer operational benefits such as enforcing distribution feeder capacity…
Evaluating long-form answers in high-stakes domains such as law or medicine remains a fundamental challenge. Standard metrics like BLEU and ROUGE fail to capture semantic correctness, and current LLM-based evaluators often reduce nuanced…
Measurements of carbon content in coal using laser-induced breakdown spectroscopy (LIBS) is limited by its low measurement precision and accuracy. A spectrum standardization method was proposed to achieve both reproducible and accurate…
In non-central heavy-ion collisions, spectator protons that do not participate in the interaction create strong magnetic fields. The strength of these fields allows testing an effect based on the hypothesized properties of QCD. The presence…
Estimating the causal effects of a spatially-varying intervention on a spatially-varying outcome may be subject to non-local confounding (NLC), a phenomenon that can bias estimates when the treatments and outcomes of a given unit are…
Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the…
Quantifying associations between short-term exposure to ambient air pollution and health outcomes is an important public health priority. Many studies have investigated the association considering delayed effects within the past few days.…
We consider the problem of conditional density estimation, which is a major topic of interest in the fields of statistical and machine learning. Our method, called Marginal Contrastive Discrimination, MCD, reformulates the conditional…
Modeling liquid crystal elastomers (LCEs) at the molecular level is crucial for the predictable design of energy-conversion and stimuli-responsive materials. Here, we develop a self-consistent field theory for LCEs which captures the…
Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the mean square error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE…
The Expected Calibration Error (ece), the dominant calibration metric in machine learning, compares predicted probabilities against empirical frequencies of binary outcomes. This is appropriate when labels are binary events. However, many…